The hottest Artificial Intelligence Substack posts right now

And their main takeaways
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Top Technology Topics

Perceptrons, XOR, and the first "AI winter"

The Counterfactual • 139 implied HN points • 17 Jan 24
🕹 Technology Artificial Intelligence
  1. AI systems are getting better, but there are still limits to what they can do. For example, some tasks might just be impossible for current AI technology.
  2. The history of AI shows that there have been times of excitement followed by periods of reduced interest, called 'AI winters'. This happens especially when expectations exceed reality.
  3. Early AI models, like perceptrons, were limited in their abilities, which led to skepticism about their potential. Understanding these past limitations helps us think more critically about today's AI capabilities.

✨ Bard diventa Gemini. La prova di Gemini Advanced

Vincos Newsletter • 117 implied HN points • 10 Feb 24
🕹 Technology Artificial Intelligence
  1. Google rebranded Bard to Gemini and launched Gemini Advanced with a more powerful language model, Gemini Ultra 1.0, tested by the author.
  2. Disney investing in the metaverse with a $1.5 billion deal with Epic Games to bring Disney, Pixar, Marvel, and Star Wars content to Fortnite.
  3. OpenAI introduces metadata for images produced with ChatGPT, Dall-E, and API, using the C2PA open standard, to track image authenticity and engagement.

Moneyballocracy

Gradient Ascendant • 20 implied HN points • 22 Dec 25
🕹 Technology Artificial Intelligence
  1. AI models are rapidly getting good at forecasting and already rival the wisdom of crowds and some human forecasters.
  2. Forecasting with AI is cheap and scalable, so you can run detailed, conditional predictions across thousands of stocks, counties, or scenarios that used to be impractical.
  3. Making the future more legible will reshape elections and politics: it can help match policy to voter preferences but also enable targeted manipulation, and any side that uses it effectively will gain a real advantage.

The Sequence Knwoledge #776: Fake It 'Til You Make It: How RL is Perfecting Synthetic Data.

TheSequence • 21 implied HN points • 23 Dec 25
🕹 Technology Artificial Intelligence
  1. Reinforcement learning environments can manufacture synthetic data by letting agents interact with simulators or APIs, producing richly labeled trajectories of states, actions, rewards, failures, and recoveries.
  2. This method is especially valuable when real data is scarce or privacy-restricted, and it shines in domains with verifiable outcomes like coding sandboxes, web automation, spreadsheets/SQL, and robotics-in-sim.
  3. Executing tasks to generate data (instead of just describing answers) gives models supervision on how to act and recover, and techniques like Reflexion can use those RL-generated trajectories to iteratively improve agents.

What’s the impact of artificial intelligence on energy demand?

Sustainability by numbers • 402 implied HN points • 18 Nov 24
🌞 Climate & Environment Artificial Intelligence
  1. AI and data centers currently use only a small portion of the world's electricity, about 1 to 2%. Even with the rise in AI, experts expect this demand to grow slowly in the coming years.
  2. People often worry about energy demands from AI, similar to past fears about data centers. However, improvements in technology and efficiency have kept actual energy use in check.
  3. The future energy demand of AI is uncertain, and while it will likely increase, it may not be as drastic as some predict. Continued efficiency improvements will be key to managing this growth.
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Microsoft builds the bomb

benn.substack • 1508 implied HN points • 26 May 23
🕹 Technology Artificial Intelligence
  1. The modern data stack aimed to revolutionize how technology is built and sold, focusing on modularity and specialized tools.
  2. Microsoft introduced Fabric as an all-in-one data and analytics platform to address the issue of fragmentation in the modern data stack.
  3. Fabric from Microsoft presents a unified solution but may risk limiting choice and innovation in the data industry.

Where Was RDNA4 at AMD’s Keynote?

More Than Moore • 326 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence
  1. AMD didn't announce RDNA4 at the CES keynote because they felt a short presentation wouldn't do it justice. They want to provide detailed information rather than leave people with questions.
  2. AMD plans to share more about RDNA4 through partners at CES, but a dedicated event will follow for an in-depth reveal. They are close to launch but wanted to wait for the right time.
  3. The naming scheme for new graphics cards will be clearer to help users make better comparisons. AMD aims to improve performance in key gaming areas and ensure good value for consumers.

Data Science Weekly - Issue 507

Data Science Weekly Newsletter • 279 implied HN points • 11 Aug 23
🕹 Technology Artificial Intelligence
  1. Large Language Models (LLMs) can take over some data tasks, but they won't replace all data jobs. Many tasks still need human insight and specialized skills.
  2. Understanding machine learning theory takes a long time, but in the industry, practical implementation is often more important. It's crucial to balance theory and hands-on skills.
  3. The new field of mechanistic interpretability is growing. Researchers are looking at how models learn and generalize, aiming to make sense of how AI works.

UX Roundup: How People Use AI | AI Does User Research | Founding Designers | Prompt Engineering | AI Agent Use Cases | Electricity Fuels AI | GPT 5.2 Beats Humans

Jakob Nielsen on UX • 23 implied HN points • 15 Dec 25
🕹 Technology Artificial Intelligence
  1. Workers are already using AI a lot — often secretly — so product design must support both automation and collaboration, teach prompting, and give users control (especially for creative workflows that need canvas-style UIs and curator tools).
  2. AI can run and analyze large-scale interviews, turning qualitative insights into quantifiable themes and making researchers into orchestrators, but agent behavior and user needs change over time so longitudinal usability studies are essential.
  3. Simple persona prompts don’t improve factual accuracy, yet models and costs are improving rapidly — cutting task costs and enabling AI to outperform experts on many half-day tasks — so designs and infrastructure (including power capacity) must evolve quickly.

Tech in 2025: Infrastructure, Innovation, and Integrity

philsiarri • 22 implied HN points • 11 Dec 25
🕹 Technology Artificial Intelligence
  1. AI became everyday infrastructure: agentic systems and wider GPU access made generative tools and smarter search part of normal workflows.
  2. Big hardware launches — like the NVIDIA RTX 5090, Galaxy Z Fold 7, and Switch 2 — pushed performance and helped new device formats reach mainstream appeal.
  3. The year favored steady integration over sudden disruption, with sustainability shifting from an aspiration to an industry requirement.

MAMLM as a General Purpose Technology: The Ghost in the GDP Machine

Brad DeLong's Grasping Reality • 130 implied HN points • 24 Jun 25
🕹 Technology Artificial Intelligence
  1. Big technology changes, like AI, often take longer to have an impact than we expect. History shows that these changes usually happen in small steps instead of all at once.
  2. The way AI is being used in businesses is growing, with more companies starting to adopt these technologies. This can lead to higher productivity over time.
  3. To really benefit from new technologies like AI, we need patience and creativity in our systems. The changes won't happen overnight, but it's important to stick with it.

Data Science Weekly - Issue 502

Data Science Weekly Newsletter • 319 implied HN points • 07 Jul 23
🕹 Technology Artificial Intelligence
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Data Science Weekly - Issue 535

Data Science Weekly Newsletter • 99 implied HN points • 23 Feb 24
🕹 Technology Artificial Intelligence
  1. Scaling AI tools like ChatGPT involves overcoming many engineering challenges to handle large user demands. It's important to manage growth effectively to keep users satisfied.
  2. There's a lot of information out there about generative AI, making it hard to keep up. A guidebook can help condense this information and provide practical insights.
  3. Linear regression is still a valuable tool in data science. Sometimes going back to basics can yield better results than relying on complex models.

Cathy reads If Anyone Builds It Everyone Dies

Sex and the State • 24 implied HN points • 02 Dec 25
🕹 Technology Artificial Intelligence
  1. I’m not convinced advanced AI will definitely kill everyone and worry that trying to stop it outright could forfeit huge potential benefits like curing disease and ending scarcity.
  2. Media and tech handling of AI is broken: coverage is shallow and companies are building capabilities faster than they understand them, so better journalism and oversight are needed.
  3. Proposals for a global pause or bans on AI are vague and problematic — it’s unclear who would write or enforce such rules, how to define forbidden "improvements," or whether the push for prohibition is driven by political or financial interests.

Data Science Weekly - Issue 491

Data Science Weekly Newsletter • 419 implied HN points • 21 Apr 23
🕹 Technology Artificial Intelligence
  1. AI academics are facing challenges keeping up with private sector investments. It's important for them to find survival strategies to remain competitive.
  2. There are ongoing discussions about the rapid progress in machine learning and how it can be overwhelming for developers. Many are sharing thoughts on adapting to this fast-paced change.
  3. Visualizing neural networks properly can help clarify concepts. There is a push for better diagrams to avoid confusion in understanding how these networks function.

🌎⏩ How fast can the global economy grow?

Faster, Please! • 822 implied HN points • 02 Mar 24
💼 Business Artificial Intelligence
  1. The concept of the Singularity involves rapid technological advancements leading to an unimaginable surge in economic growth.
  2. The idea of exponential economic growth can be challenging to comprehend, similar to how residents of a two-dimensional world might struggle to imagine a three-dimensional object.
  3. Exploring historical precedents can offer insights into more feasible scenarios for economic growth.

Google's Gemini Advanced: Tasting Notes and Implications

One Useful Thing • 861 implied HN points • 08 Feb 24
🕹 Technology Artificial Intelligence
  1. Gemini Advanced is a GPT-4 class model, offering strengths and weaknesses compared to other advanced AI models.
  2. Gemini Advanced reveals the potential for emergent properties in large AI models, showing hints of 'ghosts' or unique intelligence.
  3. Google's Gemini Advanced hints at a future where AI serves as powerful integrated personal assistants, differentiating itself from other AI models.

The Tech Buffet #17: 9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems

The Tech Buffet • 139 implied HN points • 02 Jan 24
🕹 Technology Artificial Intelligence
  1. Make sure the data you use for RAG systems is clean and accurate. If you start with bad data, you'll get bad results.
  2. Finding the right size for document chunks is important. Too small or too large can affect the quality of the information retrieved.
  3. Adding metadata to your documents can help organize search results and make them more relevant to what users are looking for.

Weekly Top Picks #96

The Algorithmic Bridge • 276 implied HN points • 03 Feb 25
🕹 Technology Artificial Intelligence
  1. OpenAI has launched two new AI agents, Operator and Deep Research, which focus on web tasks and detailed reports. Deep Research is particularly useful right now.
  2. OpenAI's o3-mini model is now free and demonstrates strong reasoning capabilities. This shows that powerful AI tools can be accessible to everyone.
  3. AI technology is evolving rapidly, and companies can benefit collectively from its advancements. Telling an AI to think longer can actually improve its performance.

Decoding Apple's AI Ambitions

Gradient Flow • 219 implied HN points • 29 Jun 23
🕹 Technology Artificial Intelligence
  1. Apple's AI focus is on Machine Learning and Computer Vision with emerging areas like Robotics and Speech Recognition, aiming to enhance services like Siri.
  2. Apple shows active interest in AI areas like Generative AI and large language models through their job postings, emphasizing deep learning skills.
  3. Apple's AI strategy integrates hardware and software to provide personalized experiences, leveraging silicon chips, Neural Engine, and fine-grained data for future AI applications.

Data Laundering: How Stability AI managed to get millions of copyrighted art works without paying artists [Finance Fridays]

Technology Made Simple • 219 implied HN points • 12 Aug 23
🕹 Technology Artificial Intelligence
  1. Data laundering involves converting stolen data to be used illegally or sold as legitimate data.
  2. Tech companies, like Stability AI, can get around artist copyright by using creative methods with AI art.
  3. It's essential to ensure fair compensation for artists and creators whose work is used, and to establish better regulations for copyright protection in data usage.

The Challenge to Human Creativity: Can AI Surpass Humans in Divergent Thinking?

UX Psychology • 218 implied HN points • 28 Sep 23
🔬 Science Artificial Intelligence
  1. Artificial intelligence (AI) is challenging the notion that creativity is solely a human trait, with recent AI systems showcasing high-quality artistic and literary works.
  2. Comparisons between human and AI creativity, particularly in divergent thinking, demonstrate that while AI excels in some aspects, highly creative humans can still make surprising connections between concepts.
  3. Creative professionals like designers, artists, and writers may find that while AI can outperform average human creative thinking, uniquely human qualities such as intuition, emotional expressiveness, and cultural embeddedness continue to set humans apart in pushing creative boundaries.

Accio Insights: The Marauder’s Map of the ML World

The Palindrome • 3 implied HN points • 19 Feb 26
🕹 Technology Artificial Intelligence
  1. Embeddings are learned, dense numerical vectors that capture what words or items mean in context instead of using one‑hot or random encodings.
  2. Similarity in embedding space is measured by the cosine of the angle between vectors, and relationships show up as directions you can add or subtract (for example, king − man + woman ≈ queen), so similar things cluster and outliers stand out.
  3. Embeddings are a core building block across ML systems — powering search, LLMs, image generators, and recommendations — and engineers must design around retrieval, scale, latency, and reliability when using them in production.

2024: The Year the iPhone is Falling Behind

Modern Value Investing • 157 implied HN points • 09 Dec 23
🕹 Technology Artificial Intelligence
  1. Google is making significant advancements in AI with the introduction of Gemini models and targeting Apple's iPhone market.
  2. Apple, despite its strong market presence, may face challenges in the AI race as its lack of innovative AI products could impact its competitive position.
  3. The future of smartphones is being reshaped by advancements in AI technology, with companies like Google and OpenAI aiming to redefine user experiences.

🤖👷‍♂️ Beyond disruption: AI, expertise, and the future of middle-class workers

Faster, Please! • 822 implied HN points • 14 Feb 24
🕹 Technology Artificial Intelligence
  1. Tech progress involves creative destruction - some jobs are lost, but new ones are created, especially in AI-related fields.
  2. Advances in artificial intelligence are reshaping the workforce as companies invest in AI systems and technologies.
  3. The impact of AI on the job market is a big question for the future - will it lead to widespread technological unemployment or follow historical patterns of job creation and loss?

Mechanisms too simple for humans to design

Telescopic Turnip • 274 implied HN points • 22 Jan 25
🔬 Science Artificial Intelligence
  1. Living organisms, like butterflies and bacteria, are incredibly complex, yet humans struggle to replicate them fully because they are surprisingly simple in construction. It's like trying to build a working insect but only using a few basic parts.
  2. The information contained in the genomes of living beings is often much less than what we assume. For example, the human genome contains less useful information than what fits on a CD, showcasing how nature efficiently packs information.
  3. Natural evolution leads to a balance where simpler designs can survive better, while human-made technologies often have complex specifications and high error rates. This means some amazing designs in nature might be too bizarre for humans to create intentionally.

AI Web scraping use-cases for Data Teams: Intelligence gathering

The Orchestra Data Leadership Newsletter • 39 implied HN points • 21 May 24
🕹 Technology Artificial Intelligence
  1. Web scraping with AI can enhance intelligence gathering by efficiently collecting and processing data from various public sources on the internet.
  2. Leveraging Large Language Models (LLMs) can improve the accuracy and robustness of web scraping systems when dealing with changes in HTML code structure.
  3. Using tools like Nimble for web scraping allows for more efficient and accurate data collection by training models on different types of websites for specific use cases.

LLMs Fight With Both Hands Tied Behind Their Back

Am I Stronger Yet? • 313 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence
  1. Large Language Models (LLMs) like o3 are becoming better at solving complex math and coding problems, showing impressive performance compared to human competitors. They can tackle hard tasks with many attempts, which is different from how humans might solve them.
  2. Despite their advances, LLMs struggle with tasks that require visual reasoning or creativity. They often fail to understand spatial relationships in images because they process information in a linear way, making it hard to work with visual puzzles.
  3. LLMs rely heavily on knowledge in their 'heads' and do not have access to real-world knowledge. When they gain access to more external tools, their performance could improve significantly, potentially changing how they solve various problems.

Facebook's Challenges

In My Tribe • 379 implied HN points • 25 Oct 24
🕹 Technology Artificial Intelligence
  1. Facebook struggles with content moderation because it has to balance user complaints. If they are too strict or too lenient, someone will be unhappy.
  2. Switching to a subscription model would likely not work well for Facebook since it would lose valuable user data that helps target ads.
  3. Facebook sees TikTok as a competitor and has changed its platform to reach users who want to connect with strangers, which has led to some issues with political content.

BLT: Byte Latent Transformer

Gonzo ML • 315 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence
  1. The Byte Latent Transformer (BLT) uses patches instead of tokens, allowing it to adapt based on the complexity of the input. This means it can process simpler inputs more efficiently and allocate more resources to complex ones.
  2. BLT can accurately encode text at a byte level, overcoming issues with traditional tokenization that often lead to mistakes in understanding languages and simple tasks like counting letters.
  3. BLT architecture has shown better performance than older models, handling tasks like translation and sequence manipulation more effectively. This advancement could improve the application of language models across different languages and reduce errors.